inception_resnet_v1.py

from __future__ import annotations

import os
from pathlib import Path
from typing import TYPE_CHECKING

import torch
from torch import nn
from torch.nn import functional as tv_functional

from .utils.download import download_url_to_file

if TYPE_CHECKING:
    from torch.nn.common_types import _size_2_t


class BasicConv2d(nn.Module):
    def __init__(  # noqa: PLR0913
        self, in_planes: int, out_planes: int, kernel_size: _size_2_t, stride: _size_2_t, padding: _size_2_t = 0
    ) -> None:
        super().__init__()
        self.conv = nn.Conv2d(
            in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, bias=False
        )  # verify bias false
        self.bn = nn.BatchNorm2d(
            out_planes,
            eps=0.001,
            momentum=0.1,
            affine=True,  # value found in tensorflow  # default pytorch value
        )
        self.relu = nn.ReLU(inplace=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x  # noqa: RET504


class Block35(nn.Module):
    def __init__(self, scale: float = 1.0) -> None:
        super().__init__()

        self.scale = scale

        self.branch0 = BasicConv2d(256, 32, kernel_size=1, stride=1)

        self.branch1 = nn.Sequential(
            BasicConv2d(256, 32, kernel_size=1, stride=1), BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
        )

        self.branch2 = nn.Sequential(
            BasicConv2d(256, 32, kernel_size=1, stride=1),
            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1),
            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1),
        )

        self.conv2d = nn.Conv2d(96, 256, kernel_size=1, stride=1)
        self.relu = nn.ReLU(inplace=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        out = torch.cat((x0, x1, x2), 1)
        out = self.conv2d(out)
        out = out * self.scale + x
        return self.relu(out)


class Block17(nn.Module):
    def __init__(self, scale: float = 1.0) -> None:
        super().__init__()

        self.scale = scale

        self.branch0 = BasicConv2d(896, 128, kernel_size=1, stride=1)

        self.branch1 = nn.Sequential(
            BasicConv2d(896, 128, kernel_size=1, stride=1),
            BasicConv2d(128, 128, kernel_size=(1, 7), stride=1, padding=(0, 3)),
            BasicConv2d(128, 128, kernel_size=(7, 1), stride=1, padding=(3, 0)),
        )

        self.conv2d = nn.Conv2d(256, 896, kernel_size=1, stride=1)
        self.relu = nn.ReLU(inplace=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        out = torch.cat((x0, x1), 1)
        out = self.conv2d(out)
        out = out * self.scale + x
        return self.relu(out)


class Block8(nn.Module):
    def __init__(self, scale: float = 1.0, *, noReLU: bool = False) -> None:  # noqa: N803
        super().__init__()

        self.scale = scale
        self.noReLU = noReLU

        self.branch0 = BasicConv2d(1792, 192, kernel_size=1, stride=1)

        self.branch1 = nn.Sequential(
            BasicConv2d(1792, 192, kernel_size=1, stride=1),
            BasicConv2d(192, 192, kernel_size=(1, 3), stride=1, padding=(0, 1)),
            BasicConv2d(192, 192, kernel_size=(3, 1), stride=1, padding=(1, 0)),
        )

        self.conv2d = nn.Conv2d(384, 1792, kernel_size=1, stride=1)
        if not self.noReLU:
            self.relu = nn.ReLU(inplace=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        out = torch.cat((x0, x1), 1)
        out = self.conv2d(out)
        out = out * self.scale + x
        if not self.noReLU:
            out = self.relu(out)
        return out


class Mixed6a(nn.Module):
    def __init__(self) -> None:
        super().__init__()

        self.branch0 = BasicConv2d(256, 384, kernel_size=3, stride=2)

        self.branch1 = nn.Sequential(
            BasicConv2d(256, 192, kernel_size=1, stride=1),
            BasicConv2d(192, 192, kernel_size=3, stride=1, padding=1),
            BasicConv2d(192, 256, kernel_size=3, stride=2),
        )

        self.branch2 = nn.MaxPool2d(3, stride=2)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        return torch.cat((x0, x1, x2), 1)


class Mixed7a(nn.Module):
    def __init__(self) -> None:
        super().__init__()

        self.branch0 = nn.Sequential(
            BasicConv2d(896, 256, kernel_size=1, stride=1), BasicConv2d(256, 384, kernel_size=3, stride=2)
        )

        self.branch1 = nn.Sequential(
            BasicConv2d(896, 256, kernel_size=1, stride=1), BasicConv2d(256, 256, kernel_size=3, stride=2)
        )

        self.branch2 = nn.Sequential(
            BasicConv2d(896, 256, kernel_size=1, stride=1),
            BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
            BasicConv2d(256, 256, kernel_size=3, stride=2),
        )

        self.branch3 = nn.MaxPool2d(3, stride=2)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x0 = self.branch0(x)
        x1 = self.branch1(x)
        x2 = self.branch2(x)
        x3 = self.branch3(x)
        return torch.cat((x0, x1, x2, x3), 1)


class InceptionResnetV1(nn.Module):
    """Inception Resnet V1 model with optional loading of pretrained weights.

    Model parameters can be loaded based on pretraining on the VGGFace2 or CASIA-Webface
    datasets. Pretrained state_dicts are automatically downloaded on model instantiation if
    requested and cached in the torch cache. Subsequent instantiations use the cache rather than
    redownloading.

    Keyword Arguments:
        pretrained {str} -- Optional pretraining dataset. Either 'vggface2' or 'casia-webface'.
            (default: {None})
        classify {bool} -- Whether the model should output classification probabilities or feature
            embeddings. (default: {False})
        num_classes {int} -- Number of output classes. If 'pretrained' is set and num_classes not
            equal to that used for the pretrained model, the final linear layer will be randomly
            initialized. (default: {None})
        dropout_prob {float} -- Dropout probability. (default: {0.6})
    """

    def __init__(  # noqa: PLR0913
        self,
        pretrained: str | None = None,
        *,
        classify: bool = False,
        num_classes: int | None = None,
        dropout_prob: float = 0.6,
        device: torch.device | str | None = None,
    ) -> None:
        super().__init__()

        # Set simple attributes
        self.pretrained = pretrained
        self.classify = classify
        self.num_classes = num_classes

        if pretrained == "vggface2":
            tmp_classes = 8631
        elif pretrained == "casia-webface":
            tmp_classes = 10575
        elif pretrained is None and self.classify and self.num_classes is None:
            msg = "If 'pretrained' is not specified and 'classify' is True, 'num_classes' must be specified"
            raise ValueError(msg)

        # Define layers
        self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)
        self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
        self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.maxpool_3a = nn.MaxPool2d(3, stride=2)
        self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
        self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
        self.conv2d_4b = BasicConv2d(192, 256, kernel_size=3, stride=2)
        self.repeat_1 = nn.Sequential(
            Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17), Block35(scale=0.17)
        )
        self.mixed_6a = Mixed6a()
        self.repeat_2 = nn.Sequential(
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
            Block17(scale=0.10),
        )
        self.mixed_7a = Mixed7a()
        self.repeat_3 = nn.Sequential(
            Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20), Block8(scale=0.20)
        )
        self.block8 = Block8(noReLU=True)
        self.avgpool_1a = nn.AdaptiveAvgPool2d(1)
        self.dropout = nn.Dropout(dropout_prob)
        self.last_linear = nn.Linear(1792, 512, bias=False)
        self.last_bn = nn.BatchNorm1d(512, eps=0.001, momentum=0.1, affine=True)

        if pretrained is not None:
            self.logits = nn.Linear(512, tmp_classes)
            load_weights(self, pretrained)

        if self.classify and self.num_classes is not None:
            self.logits = nn.Linear(512, self.num_classes)

        self.device = torch.device("cpu")
        if device is not None:
            self.device = device
            self.to(device)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Calculate embeddings or logits given a batch of input image tensors.

        Arguments:
            x {torch.tensor} -- Batch of image tensors representing faces.

        Returns:
            torch.tensor -- Batch of embedding vectors or multinomial logits.
        """
        x = self.conv2d_1a(x)
        x = self.conv2d_2a(x)
        x = self.conv2d_2b(x)
        x = self.maxpool_3a(x)
        x = self.conv2d_3b(x)
        x = self.conv2d_4a(x)
        x = self.conv2d_4b(x)
        x = self.repeat_1(x)
        x = self.mixed_6a(x)
        x = self.repeat_2(x)
        x = self.mixed_7a(x)
        x = self.repeat_3(x)
        x = self.block8(x)
        x = self.avgpool_1a(x)
        x = self.dropout(x)
        x = self.last_linear(x.view(x.shape[0], -1))
        x = self.last_bn(x)
        x = self.logits(x) if self.classify else tv_functional.normalize(x, p=2, dim=1)
        return x


def load_weights(mdl: torch.nn.Module, name: str) -> None:
    """Download pretrained state_dict and load into model.

    Arguments:
        mdl {torch.nn.Module} -- Pytorch model.
        name {str} -- Name of dataset that was used to generate pretrained state_dict.

    Raises:
        ValueError: If 'pretrained' not equal to 'vggface2' or 'casia-webface'.
    """
    if name == "vggface2":
        path = "https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180402-114759-vggface2.pt"
        hash_prefix = "281cebca8662831adb987a874bdcb36e73f5b1c6dc5ee5878f305e985625d99b"
    elif name == "casia-webface":
        path = "https://github.com/timesler/facenet-pytorch/releases/download/v2.2.9/20180408-102900-casia-webface.pt"
        hash_prefix = "7a67afdbbc995fce5e10128675e318799a70698c2f433ba75dd7eb9a2f096e7d"
    else:
        msg = "Pretrained models only exist for 'vggface2' and 'casia-webface'"
        raise ValueError(msg)

    model_dir = get_torch_home() / "checkpoints"
    model_dir.mkdir(parents=True, exist_ok=True)

    cached_file = model_dir / Path(path).name
    if not cached_file.exists():
        download_url_to_file(path, cached_file, hash_prefix)

    state_dict = torch.load(cached_file)
    mdl.load_state_dict(state_dict)


def get_torch_home() -> Path:
    """Get the torch cache directory."""
    return Path(os.getenv("TORCH_HOME", Path(os.getenv("XDG_CACHE_HOME", "~/.cache")) / "torch")).expanduser()